Autoencoder-based NNs for m-MIMO CSI feedback [1]}, [2]}-[3]} utilize convolutional layers in the encoder to extract features before a fully-connected layer, where the disadvantage lies in that the compression is done by the fully-connected layer alone. Note that a single fully-connected layer results in a sharp dimension reduction, leading to excessive impairment of the CSI information. Moreover, the parameters of the fully-connected layer constitutes the majority of the entire network at low compression ratios, which remains large if no compression is done before the fully-connected layer. Taking the correlation difference into consideration, this disadvantage can be well addressed. From the perspective of information theory, the domain with a stronger correlation, i.e., the angular domain, can be compressed to a greater extent. Therefore, it is natural to adopt different compression strategies for the angular and delay domains. Our ENet compresses the CSI matrix in the angular domain before the fully-connected layer.
